Comprehensive analysis of preparation strategies for phase change nanocomposites and nanofluids with brief overview of safety equipment

Jurčević, Mišo; Nižetić, Sandro; Arıcı, Müslüm; Ocłoń, Paweł

doi:10.1016/j.jclepro.2020.122963

Cited by 48 publications

(9 citation statements)

References 68 publications

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“…[22][23][24][25][26][27][28][29][30][31][32][33] Consequently, it is also interpreted that the results predicted by the neural network influenced by the abovementioned parameters. Other parameters such as the preparation process of nano-PCM 68 and measurement method 69 can also affect its TC.…”

Section: Data Validationmentioning

confidence: 99%

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

Motahar

Sadri

2021

Intl J of Energy Research

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The weak thermal conductance of a phase change material (PCM) can be intensified by dispersing nanostructured materials called nano-PCM. Accurate thermal conductivity (TC) prediction of nano-PCM is essential to evaluate heat transport during phase change processes, namely, melting and solidification. The present study develops an artificial neural network (ANN) to forecast the TC of n-octadecane as a PCM with dispersed oxide nanoparticles. A total of 122 experimental datasets from existing literature with a wide range of temperatures (5-60 C), nanoparticles (CuO, Al 2 O 3 , TiO 2 , and mesoporous SiO 2 ), nanoparticle mass fractions (0.5-12 wt%) are compiled to train a multi-layered feed-forward ANN with Levenberg-Marquardt back-propagation algorithm.An optimal architecture of the neural network is acquired by varying the number of network hidden layers, the number of neurons in each layer, and the transfer function of layers. The minimum mean square error (MSE) of 1.3512 Â 10 À5 is obtained for the best developed ANN. Results show that average absolute deviation (AAD) of 0.002458, mean absolute percentage error (MAPE) of 0.8260%, and correlation coefficient (R) of 0.999964948 are achieved for training data. Moreover, MAPE, AAD, and R values are, respectively, 0.9478, 0.002167, and 0.9999715861 for testing data. The maximum percentage errors of ANN computed values are 2.31%, and 0.812% for liquid and solid phases, respectively. This indicates that the ANN model accurately predicts the enhanced TC of nano-PCM across various oxide nanoparticles, temperatures, and nanoparticle loadings.

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Section: Data Validationmentioning

confidence: 99%

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

Motahar

Sadri

2021

Intl J of Energy Research

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“…More details on this subject can be found in [30][31][32]. Many researchers highly recommend the two-steps method as it showed better characteristics of the prepared NFs as well as its economic advantage.…”

Section: Classification and Preparation Of Nfsmentioning

confidence: 99%

Single and Hybrid Nanofluids to Enhance Performance of Flat Plate Solar Collectors: Application and Obstacles

Al-Yasiri

Szabó

Arıcı

2020

Period. Polytech. Mech. Eng.

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Solar energy represents the best alternative for traditional energy sources used in many thermal energy systems. Among solar thermal systems, Flat Plate Solar Collectors (FPSCs) are the most utilized type implemented in low and medium-level thermal domestic applications. Recently, the usage of nanofluids (NFs) to enhance FPSCs is one of the newest technologies that has drawn the attention of researchers to improve the overall thermal efficiency of solar systems. This paper briefly reviews the recent studies carried on thermal performance enhancement of FPSCs by implementing NFs (single and hybrid NFs) considering the main influential parameters such as particle concentration, particle size, and collector area. Finally, the main obstacles reported by the researchers such as the instability, viscosity, concentration limit, corrosion effect and others are identified, which is believed to be useful for interested newcomers in this research area. Based on the studies investigated in this paper, NFs, even under low concentrations, can remarkably improve the energetic and exergetic efficiency of FPSCs.

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“…In the case of PV panel cooling, Nano-enhanced PCMs are usually considered, or in other cases various nanofluids. The important issue with nanofluids is the preparation procedure since it can strongly affect the properties and long term stability of nanoenhanched systems, [66]. The cooling of a concentrator PV (CPV) with nanofluids was discussed in study [67].…”

Section: Cooling With Nanofluidsmentioning

confidence: 99%

THERMAL MANAGEMENT OF SILICON PHOTOVOLTAIC PANELS: A REVIEW (In the press)

Nižetić¹

2020

REFFIT

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Photovoltaic (PV) technologies represent a key role in the ongoing energy transition towards the decarbonisation of convectional power systems and to reduce the harmful population impact to environment. Nowadays, the majority of market available photovoltaic PV technologies are silicon based with a usual energy conversion efficiency of less than 20%. The major drawbacks of the widely used silicon PV technologies are related to performance degradation due to aging as well as performance drops that occur during periods of elevated operating temperatures. In order to improve performance, as well as the lifetime of the PV systems, various cooling techniques have been investigated in the last two decades. The main goal of the specific cooling approaches for PV panels is to ensure efficient thermal management, as well as economic suitability. In this review paper, different cooling strategies are categorized, discussed and thoroughly elaborated in order to provide deep insight related to an expected performance improvement and economic viability. The main results of this review indicate that the cooling approaches for PVs can ensure a performance improvement ranging from about 3% up to 30%, depending if passive or active cooling approaches are applied. The main results also indicate that the economic viability as well as environmental suitability of the specific cooling approaches is not sufficiently discussed in the existing research literature.

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Comprehensive analysis of preparation strategies for phase change nanocomposites and nanofluids with brief overview of safety equipment

Cited by 48 publications

References 68 publications

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

Single and Hybrid Nanofluids to Enhance Performance of Flat Plate Solar Collectors: Application and Obstacles

THERMAL MANAGEMENT OF SILICON PHOTOVOLTAIC PANELS: A REVIEW (In the press)

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